Studying the impact of COVID-19 on the European Air Transportation Network

The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network. © 2023 IEEE.

Risk Modeling Framework for Strategic and Operational Intervention to Enhance the Effectiveness of a Closed-Loop Supply Chain

In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE

Structure, Stability, Persistence and Entropy of Stock Networks During Financial Crises

We investigate the network structures of stocks in SET100, NASDAQ100, and FTSE100 from 2006 to 2022, using the correlation distance and the time-space average of correlations as a threshold for connectivity of two stocks. Structure, stability, multifractality, and entropy of the networks are investigated to compare their behaviors before and after financial crises. The results show that during high volatility periods, such as the global financial crisis in 2008 and the COVID pandemic in 2020, the network characteristic path length decreases, while the clustering coefficient increases, suggesting that the network has shrunk in size, and stocks become tightly linked, similar to trends of price and return behaviors observed in many stocks during financial crises. Furthermore, the minimal level of network entropy implies that the market network stability decreases, and each sector has lost its ability to perform independently. We also find that the persistence of the network structure and the network entropy in SET increase during a period of high volatility as evident by a significant increase of the Holder exponent, while results from NASDAQ and FTSE do not exhibit such pronounced behavior, possibly due to having higher market fluctuation. Network features of SET and FTSE show recovery of same values after the 2008 crisis faster than NASDAQ, and in less than 100 trading days;however, they exhibit slower recovery, except for the network entropy, from the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Reviving the economy while saving lives: a deep reinforcement learning approach for smart POI reopening

With the gradual improvements in COVID-19 metrics and the accelerated immunization progress, countries around the world have began to focus on reviving the economy while continuously strengthening epidemic control. POInt-of-Interest (POI) reopening, as a necessity for restoring human mobilities, has become a crucial step to recouple economic recovery and public health management. In contrast to the lock-down policy, POI reopening demands a dynamic trade-off between epidemic interventions and economic costs. In the urban scenario, there exist three key challenges in developing effective POI reopening strategies as follows. (1) During the POI reopening process, there are multiple urban factors affecting the epidemic transmission, which are difficult to simultaneously incorporate and balance in a single reopening strategy;(2) the effects of POI reopening on both economic recovery and epidemic control are long-term, which are hard to capture by static models;and (3) the dual objectives of minimizing infections and maintaining POIs' visits are conflicting, making it difficult to achieve a flexible and scalable trade-off. To tackle the above challenges, we propose Reopener, a deep reinforcement learning (RL) framework for smart POI reopening. First, we utilize a bipartite graph neural network to automatically encode all urban factors that would affect the epidemic prevention and POI visit restriction. Second, we employ a RL-based deep policy network to enable flexible updates in restrictions on POIs along with the trend of epidemic. Third, we design a novel reward function to guide the RL agent to learn smartly, thus comprehensively trading off infections and visit sustainability of POIs. Extensive experimental results demonstrate that Reopener outperforms all baseline methods with remarkable improvements, by reducing the overall economic cost by at least 6.42%. Reopener can effectively suppress infections and support a phase-based POI reopening process, which provides valuable insights for strategy design in post-COVID-19 economic recovery. © 2022 Owner/Author.

The Impact of COVID-19 on Road Traffic Accidents: A Case Study of Severely affected City in China

People's travel has changed greatly under the impact of COVID-19. However, it is controversial that whether traffic restrictions of COVID-19 have a positive or negative impact on traffic accidents. At present, there are few studies on the variations of traffic accidents under the impact of COVID-19 in China, and quantitative analysis is rare. Therefore, this study explores the traffic accidents characteristics of W city seriously affected COVID-19. Based on wavelet transform, traffic accident prediction model is established using property damage only accidents data to predict accident frequency without the impact of COVID-19. Compared with the actual traffic accidents frequency, this paper quantitatively analyzes the impact of COVID-19 on traffic accident. The results show that traffic accidents show a trend of decline-bottom-recovery;the frequency of accidents after the recovery is more than the previous year's level;compared with other periods in 2020, the proportion of injury accidents increased sharply during the period when traffic restrictions were gradually loose. The result of accident prediction shows that BP neural network has the best prediction effect. After the implementation of traffic restrictions, the frequency of accidents shows three stages: rapid decline, bottom and continuous rise. In the three stages, the frequency of property damage only accidents decreased by 379.06, 654.72 and 288.19 per day on average. © 2022 IEEE.

Application of Temporal Network on Potential Disease Transmission: Hospital Case Study

Early diagnosis of potential epidemic transmission of diseases such as influenza or coronavirus in hospitals where one-to-one contact occurs is central not only to save patients’ life, but also to prevent disease propagation to staff, nurses, medical doctors, and other workers. This paper aims to predict the risk threshold of influenza disease transmission in a temporal network;the hospital’s data in Lyon, France is taken as a case study. The network involves 46 health care workers and 29 patients. The Susceptible Infectious Recovered (SIR) model is used for the analysis. The SIR model is more fit for the influenza disease because a patient is not suspected to spread the disease after recovery. The results show that the disease propagation rate is lower in the temporal network compared with the corresponding aggregated network. It is found out that that the threshold of an epidemic occurs when the transmission percentage is 10%. Most importantly, it is found that the nurses and administrators are more likely to be infected than physicians or patients in this case study. The proposed model is applicable in hospitals, schools, or any work organization for epidemiologic control. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.